In 2007, a cash-strapped Brian Chesky came up with a shrewd way to pay his $1,200 San Francisco apartment rent. He would offer “Air bed and breakfast”, which consisted of three airbeds,...

The majority of guests on Airbnb are women. Most Airbnb guests are aged 25 to 34.

This is the complete breakdown of how much revenue Airbnb makes in commission from listings in each region.

In New York City, one of the United States’ most iconic destinations, Airbnb has established itself as a key player in the accommodation market. In 2025, Airbnb customers booked an average of ** nights per stay, with an average price of *** U.S. dollars per night. Meanwhile, the average income per property was ***** U.S. dollars that year. Are Airbnb rentals expensive in New York City? As of early 2024, the most expensive Airbnb properties per night in the United States were in *************. This was followed by *************************. In comparison, the average cost of a night’s stay at an Airbnb property in New York City is less than half of the cost of a night in *************. How many Airbnb properties are there in New York City? In early 2024, the Airbnb market in New York City offered more than **** thousand properties accommodating to the different needs of visitors to the city. There are various types of Airbnb properties in New York City, the most common of which were entire homes and apartments, followed by private rooms. The majority of Airbnb listings also catered for longer-term stays, in light of city regulations on housing.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2024, North America had the largest regional share of gross booking value at ** percent.

The region with the most nights and experiences booked with Airbnb worldwide in 2024 was Europe, the Middle East, and Africa (or EMEA). That year, the EMEA region reported *** million bookings. Asia Pacific had the lowest number of bookings at ** million. The Asia Pacific region also had the lowest average number of nights per Airbnb booking in 2024.

📊 The Dataset

The data used for this project is sourced from a publicly available Airbnb Listings dataset. The dataset contains over 560,000 records across 10 major cities, including Paris. For this project, the data is filtered to focus solely on Paris listings.

Key Fields:

host_since: Date when the host started listing on Airbnb

neighbourhood: The neighborhood where the listing is located

price: The price per night for the listing

accommodates: Number of people the listing can accommodate

The Dataset Column Descriptions

host_since - Date when the host joined the Airbnb platform. neighbourhood - Name of the neighborhood in Paris where the listing is located. city - City name. This dataset is filtered for Paris listings only. accommodates - The maximum number of guests the listing can accommodate. price - Price per night for the listing in USD. room_type - Type of room offered in the listing (e.g., Entire home/apt, Private room, Shared room). availability_365 - Number of days the listing is available for booking throughout the year. number_of_reviews - Total number of reviews the listing has received. review_scores_rating - Average rating score given by guests for the listing (out of 100). minimum_nights - Minimum number of nights required for booking the listing. host_listings_count - Number of listings managed by the host. latitude - Latitude coordinate of the listing. longitude - Longitude coordinate of the listing.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
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Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
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  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
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Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. In 2024, Airbnb reported a gross booking value of ***** billion U.S. dollars, an increase over the previous year.

Property managers or investors can maximize rental property revenue with timely data on the region’s pricing and inventory. Equity portfolio managers can track Airbnb's key operating metrics on a timely basis and gain early insights of the company’s revenue trend.

The vacation rental market is experiencing robust growth, driven by increasing disposable incomes, a preference for unique travel experiences, and the rise of the sharing economy. The market's expansion is fueled by several key trends: the increasing popularity of alternative accommodations beyond traditional hotels, the growing adoption of online booking platforms, and the diversification of rental options, ranging from apartments and private homes to unique properties like villas and cabins. Technological advancements, such as improved search functionalities and mobile booking apps, are further enhancing accessibility and convenience for travelers. While factors like fluctuating travel restrictions and economic downturns can pose challenges, the market's inherent resilience and the continued demand for flexible and personalized travel experiences suggest a positive long-term outlook. The segmentation within the market indicates strong performance across various applications, including the travel industry and commercial sectors, with apartment and private home rentals holding significant market share. Major players like Airbnb, Booking Holdings, and Expedia are key contributors to market growth, constantly innovating to attract and retain customers. Geographic data indicates North America and Europe as major revenue generators, although Asia Pacific and other regions are showing significant growth potential. The forecast period (2025-2033) suggests continued expansion, driven by consistent demand and technological enhancements. Competition is fierce but opportunities abound for established players and new entrants alike who can effectively leverage technology and cater to evolving traveler preferences. The competitive landscape is dynamic, with established players like Airbnb and Booking Holdings continuously innovating their platforms and services to remain competitive. New entrants are also emerging, leveraging technological advancements and specialized offerings to carve out niche markets. However, regulatory challenges, particularly concerning licensing and taxation, represent a significant restraint for the industry. Maintaining sustainable practices and addressing environmental concerns are also becoming increasingly important. Despite these challenges, the long-term growth trajectory for the vacation rental market remains optimistic, propelled by persistent demand for unique and personalized travel experiences, and the ongoing evolution of technology within the hospitality sector. Data suggests that this market will experience significant growth across all regions, with some exhibiting faster growth rates than others. Understanding the nuances of regional demand and preferences will be crucial for success in this dynamic market.

According to a June 2025 analysis, Rome reported the highest number of Airbnb listings among the selected Italian cities. As of that month, there were over ****** establishments listed on Airbnb in the Italian capital. Milan and Florence followed behind, with over ****** and almost ****** listings on Airbnb. What are the leading brands for accommodation bookings in Italy? According to the Statista Consumer Insights Global survey, Airbnb was the second most popular brand for hotel and private accommodation online bookings in Italy in 2025, with over ********* of respondents having booked accommodation via that website. That year, Booking.com topped the ranking, with almost ************** of the sample reporting using that provider. Booking Holdings vs. Airbnb Booking Holdings, which operates the Booking.com brand, and Airbnb are among the biggest companies in the online travel market. In 2025, Booking Holdings had the highest market cap of the leading online travel companies worldwide, while Airbnb ranked second. Both companies experienced an annual increase in earnings in 2024. That year, Booking Holdings' revenue peaked at almost ** billion U.S. dollars. Meanwhile, Airbnb's revenue also reached an all-time high in 2024.

This data provides a detailed window into how travelers across Europe are making choices between Airbnb, boutique hotels, and chain hotels, and how those choices are influenced by perceived value, authenticity, and price sensitivity. It spans major tourism markets such as Paris, Barcelona, Rome, Berlin, Amsterdam, Vienna, Prague, Lisbon, Athens, and Dubrovnik, while layering in demographic details including age, income, and household type. By capturing these sentiment drivers alongside actual accommodation choice percentages, the data goes beyond occupancy statistics or market reports and instead reveals the deeper psychology of why travelers choose where to stay.

At its heart, the data measures the trade-offs travelers make. Some value price above all else, seeking the cheapest option and showing high sensitivity to even small changes in nightly rates. Others prioritize authenticity, looking for cultural immersion, unique architecture, or a connection to the community, a sentiment often tied to boutique hotels or Airbnb stays. Still others rate perceived value, balancing comfort, service, and cost in ways that may lean toward chain hotels where consistency and loyalty programs come into play. By quantifying these three sentiment drivers alongside accommodation choice, the data enables a holistic view of the European hospitality landscape that is not just descriptive but predictive.

For hotel operators, this data provides granular competitive intelligence. A chain hotel executive in Berlin can see not only how many travelers are opting for chain hotels versus Airbnb or boutiques, but also the sentiment scores that drive those choices. If authenticity consistently scores low for chain hotels, it suggests a strategic opening to localize offerings, integrate cultural experiences, or adjust marketing. Boutique hotel managers in Lisbon can benchmark how their authenticity score compares to Airbnb in the same city, providing evidence for whether they should double down on differentiation or compete more aggressively on price. Airbnb hosts and platform managers can assess whether travelers in cities like Athens or Dubrovnik are primarily choosing Airbnb for price sensitivity or for perceived authenticity, and then adjust host guidelines and search rankings to align with those motivations.

Tourism boards and city governments can use this data to shape destination strategies. In cities where authenticity is highly valued, they may promote cultural experiences and boutique stays that highlight heritage and local life. In cities where price sensitivity dominates, they may anticipate pressure on affordability and design policies to balance visitor demand with resident quality of life. Tracking sentiment alongside accommodation choice allows policymakers to see whether interventions such as limiting Airbnb licenses or incentivizing boutique hotels are having the intended effect.

For travel agencies and online booking platforms, this data provides immediate commercial value by informing recommendation algorithms. If Millennials traveling to Barcelona are shown to favor Airbnb due to high authenticity scores, platforms can tailor recommendations to match those preferences and increase conversion rates. If Boomers traveling to Vienna demonstrate high perceived value scores for chain hotels, agencies can design targeted campaigns that emphasize comfort, service, and reliability. By embedding demographic segmentation, the data enables personalization that goes beyond generic marketing and aligns with actual consumer psychology.

Investors and financial analysts also gain critical foresight from this data. The growth of Airbnb has often been framed in broad, disruptive terms, but this data dissects the nuance of where Airbnb’s advantage comes from and how strong it is in different markets. In Amsterdam, for example, Airbnb may dominate with authenticity but show weaker perceived value compared to boutique hotels. In Prague, chain hotels may hold firm due to loyalty programs and price competitiveness. Understanding these dynamics city by city allows investors to make sharper decisions about where to allocate capital, which hotel groups are most resilient, and where regulatory risks may matter most.

Marketing agencies and brand strategists can mine the sentiment scores for creative direction. A boutique hotel in Lisbon may craft campaigns around the theme of authenticity if the data shows that is the strongest differentiator for their target demographic. A chain hotel group in Rome might emphasize value and consistency if those resonate more strongly with middle-income families. Airbnb itself can use the data to position its brand differently across cities, leaning into affordability in one market and cultural immersion in another. The combination of quantitative percentages and sentiment scores creates a bridge between analytics and storytelling, enabling brands to market with evidence rather than assumption.

The demo...

According to data provided by Airbtics.com, Airbnb bookings in Osaka declined by almost ** percent in the week of August 23, 2020 compared to the same period in the previous year. The annual growth rate in Airbnb bookings processed in 2020 shrank significantly from February onwards due to the gradual enforcement of travel restrictions amid the global coronavirus (COVID-19) pandemic.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. The platform also allows consumers to book "experiences" in the regions they visit. In 2024, Airbnb reported over *** million booked nights and experiences. How much revenue does Airbnb make? In 2024, the total revenue of Airbnb worldwide increased by nearly ten percent over the previous year. This continued the upward trend which the company has experienced since recovering from the coronavirus (COVID-19) pandemic. ************* generated the highest share of Airbnb’s worldwide revenue in 2024, at **** billion U.S. dollars. How many people visit the Airbnb website? Airbnb ranked ***** among the most popular travel and tourism websites worldwide based on average monthly visits, behind *******************************. In 2024, airbnb.com saw its highest number of unique global visitors in March, at *** million. Meanwhile, Airbnb ranked fourth among leading travel apps globally, with over ** million downloads in 2024.

DATAANT provides the ability to extract travel data from public sources like: - Hotel websites - Flight aggregators - Homestay marketplaces - Experience marketplaces - Online Travel Agencies (OTA) and any open travel industry website you need.

Forecast travel trends with Booking.com, Airbnb, and travel aggregators data.

We support providing both raw and structured data with various delivery methods.

Get the competitive advantage of hospitality and travel Intelligence by scheduled data extractions and receive your data right to your inbox.

Metrics that can be unearthed will be ones contained in the email booking invoice such as Hotel name, type of room, dates, check in and check out times, price paid, duration of stay. We can go back to 5 years of history.

We also have cancellation emails.

Any hotel vendor can be requested too. We will conduct a search in our database to see if it justifies a parser build to extract the data.

Please contact michelle@measurable.ai for a demo or more data samples.

Airbnb, a home sharing economy platform, gives users an alternative to traditional hotel accommodation by allowing them to rent accommodation from people who are willing to share their homes. North America averaged *** nights per Airbnb booking in 2024, more than any other region that year

The aim of tourism statistics is to provide a full picture of tourism levels and trends in a comparable way across EU countries. Tourism statistics may be used to answer questions such as: what are the top regions in the EU for international tourists? how many Europeans go on holidays in their own country, and what means of transport do they use? how many stays in short-term rentals in major cities are booked via online platforms? how much does the tourism sector contribute directly to the EU's gross value added? Data sources Tourism statistics are collected via monthly and quarterly surveys. This depends on the topic and the country running the survey. The data on online platforms is based on data sharing agreements with 4 major international platforms offering short-stay accommodation. These are Airbnb, Booking.com, Tripadvisor, and Expedia Group.

The Vacation Rental Platforms market has emerged as a pivotal segment within the broader travel and hospitality industry, revolutionizing how travelers access accommodations. With the rise of sharing economy models, platforms like Airbnb, Vrbo, and Booking.com have enhanced the convenience and diversity of lodging o